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ayameRushia/wav2vec2-large-xls-r-300m-ia
d2cddb054cb1b8f530ccaff34e0360ccc1274cf8
2022-03-23T18:29:54.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "ia", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ayameRushia
null
ayameRushia/wav2vec2-large-xls-r-300m-ia
4
null
transformers
18,400
--- language: - ia license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: wav2vec2-large-xls-r-300m-ia results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: ia metrics: - name: Test WER using LM type: wer value: 8.6074 - name: Test CER using LM type: cer value: 2.4147 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-ia This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1452 - Wer: 0.1253 ## Training Procedure Training is conducted in Google Colab, the training notebook provided in the repo ## Training and evaluation data Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua) Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_ia.ipynb" Test WER without LM wer = 20.1776 % cer = 4.7205 % Test WER using wer = 8.6074 % cer = 2.4147 % evaluation using eval.py ``` huggingface-cli login #login to huggingface for getting auth token to access the common voice v8 #running with LM python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test # running without LM python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test --greedy ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.432 | 1.87 | 400 | 2.9636 | 1.0 | | 2.6922 | 3.74 | 800 | 2.2111 | 0.9977 | | 1.2581 | 5.61 | 1200 | 0.4864 | 0.4028 | | 0.6232 | 7.48 | 1600 | 0.2807 | 0.2413 | | 0.4479 | 9.35 | 2000 | 0.2219 | 0.1885 | | 0.3654 | 11.21 | 2400 | 0.1886 | 0.1606 | | 0.323 | 13.08 | 2800 | 0.1716 | 0.1444 | | 0.2935 | 14.95 | 3200 | 0.1687 | 0.1443 | | 0.2707 | 16.82 | 3600 | 0.1632 | 0.1382 | | 0.2559 | 18.69 | 4000 | 0.1507 | 0.1337 | | 0.2433 | 20.56 | 4400 | 0.1572 | 0.1358 | | 0.2338 | 22.43 | 4800 | 0.1489 | 0.1305 | | 0.2258 | 24.3 | 5200 | 0.1485 | 0.1278 | | 0.2218 | 26.17 | 5600 | 0.1470 | 0.1272 | | 0.2169 | 28.04 | 6000 | 0.1470 | 0.1270 | | 0.2117 | 29.91 | 6400 | 0.1452 | 0.1253 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
azunre/wav2vec2large-xlsr-akan
b806137e3f6f25c3a61172d1bb9576f0cce8cc2b
2021-07-05T22:35:12.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "tw", "dataset:common_voice", "transformers", "speech", "audio" ]
automatic-speech-recognition
false
azunre
null
azunre/wav2vec2large-xlsr-akan
4
null
transformers
18,401
--- language: tw datasets: - common_voice tags: - speech - audio - automatic-speech-recognition ---
azuur/wav2vec2-base-gn-demo
9b519d86652fc08af7eff2d7c355ea8a0db042f7
2022-03-24T11:57:52.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gn", "dataset:common_voice", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "generated_from_trainer", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
azuur
null
azuur/wav2vec2-base-gn-demo
4
null
transformers
18,402
--- license: apache-2.0 language: - gn tags: - generated_from_trainer - mozilla-foundation/common_voice_8_0 - robust-speech-event - hf-asr-leaderboard datasets: - common_voice - mozilla-foundation/common_voice_8_0 model-index: - name: wav2vec2-base-gn-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-gn-demo This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.7426 - Wer: 0.7256 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 50 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 4.0 | 100 | 0.7045 | 0.7409 | | No log | 8.0 | 200 | 0.7200 | 0.75 | | No log | 12.0 | 300 | 0.7400 | 0.7439 | | No log | 16.0 | 400 | 0.7677 | 0.7515 | | 0.0846 | 20.0 | 500 | 0.7765 | 0.7271 | | 0.0846 | 24.0 | 600 | 0.7821 | 0.7287 | | 0.0846 | 28.0 | 700 | 0.7671 | 0.7180 | | 0.0846 | 32.0 | 800 | 0.7594 | 0.7180 | | 0.0846 | 36.0 | 900 | 0.7500 | 0.7165 | | 0.0713 | 40.0 | 1000 | 0.7351 | 0.7287 | | 0.0713 | 44.0 | 1100 | 0.7361 | 0.7241 | | 0.0713 | 48.0 | 1200 | 0.7389 | 0.7378 | | 0.0713 | 52.0 | 1300 | 0.7424 | 0.7210 | | 0.0713 | 56.0 | 1400 | 0.7425 | 0.7256 | | 0.0669 | 60.0 | 1500 | 0.7426 | 0.7256 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3
baffo32/genji-python-6B-split
85b14e26f7946fe5d892834783cba343760000ba
2021-08-21T13:33:22.000Z
[ "gpt_neo", "text-generation", "en", "dataset:the Pile", "arxiv:2104.09864", "transformers", "pytorch", "causal-lm", "license:apache-2.0" ]
text-generation
false
baffo32
null
baffo32/genji-python-6B-split
4
null
transformers
18,403
--- language: - en tags: - pytorch - causal-lm license: apache-2.0 datasets: - the Pile --- # Genji-python 6B For example usage or to easily use the model you can check our colab notebook: [Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing) ## Model Description Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size. Split model has the checkpoints splitted, which makes it use less system RAM while loading and makes it faster to load. This model needs more effort to set up as you need to install git-lfs and pull the repo. | Hyperparameter | Value | |-------------------|--------| | n_parameters | 6,053,381,344 | | n_layers | 28* | | d_model | 4,096 | | d_ff | 16,384 | | n_heads | 16 | | d_head | 256 | | n_ctx | 2,048 | | n_vocab | 50,400 (same tokenizer as GPT-2/3) | | position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) | | RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) | `*` each layer consists of one feedforward block and one self attention block The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. ## Training data GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile. ## Training procedure Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06 ## Intended Use This model is trained for assistence on writing python code and having fun trying weird stuff with it. ### How to use This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable. For now, you need to use this fork: [Fork](https://github.com/finetuneanon/transformers) to install with pip: ```bash pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b ``` **git-lfs** also needs to be installed, on ubuntu: ```bash apt install git-lfs ``` after it's installed, initialize git-lfs: ```bash git lfs install ``` then clone this repo: ```bash git clone https://huggingface.co/NovelAI/genji-python-6B-split ``` Now we can load the model. We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards. How to use: ```python from transformers import ( AutoTokenizer, AutoModelForCausalLM, GPTNeoForCausalLM, ) model = AutoModelForCausalLM.from_pretrained("genji-python-6B-split/model").half().eval().cuda() tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") text = '''def print_customer_name''' tokens = tokenizer(text, return_tensors="pt").input_ids generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id) last_tokens = generated_tokens[0][len(tokens[0]):] generated_text = tokenizer.decode(last_tokens) print("Generation:\n" + generated_text) ``` When ran, this code generates: ```python Prompt: def print_customer_name Generation: (self, customer): """Print the name of a customer.""" if not self.is_valid(): return print("Customer: {}".format(customer)) ``` For example usage, you can see our colab notebook as well: [Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing) ## Eval results TBD ## Acknowledgements This project was possible because of the compute provided by the [TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B. Thanks to everyone who contributed to this project: - [Aero](https://github.com/AeroScripts) - [Finetune](https://github.com/finetuneanon) - [Kurumuz](https://github.com/kurumuz)
baihaisheng/bert_finetuning_test
1b41e044c3c2d5442cc99f01715c95f1a093999c
2021-05-19T12:07:08.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
baihaisheng
null
baihaisheng/bert_finetuning_test
4
null
transformers
18,404
Entry not found
banri/distilbert-base-uncased-finetuned-cola
c39594959dd1ee8951951e5cb44217978db0895f
2021-11-13T09:52:45.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
banri
null
banri/distilbert-base-uncased-finetuned-cola
4
null
transformers
18,405
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5258663312307151 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7523 - Matthews Correlation: 0.5259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.533 | 1.0 | 535 | 0.5318 | 0.3887 | | 0.3562 | 2.0 | 1070 | 0.5145 | 0.5100 | | 0.2429 | 3.0 | 1605 | 0.6558 | 0.4888 | | 0.1831 | 4.0 | 2140 | 0.7523 | 0.5259 | | 0.1352 | 5.0 | 2675 | 0.8406 | 0.5182 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
bchan007/fnctech
8280dcd1b6066960e276b1c6c9f6d6fd3e524637
2022-02-17T05:25:26.000Z
[ "pytorch", "mpnet", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
bchan007
null
bchan007/fnctech
4
null
sentence-transformers
18,406
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # bchan007/fnctech This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('bchan007/fnctech') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('bchan007/fnctech') model = AutoModel.from_pretrained('bchan007/fnctech') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=bchan007/fnctech) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
bella/bert_finetuning_test
b5400fb34c9a6869ace19376f700fe4fa8a194c4
2021-05-19T12:27:44.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
bella
null
bella/bert_finetuning_test
4
null
transformers
18,407
Entry not found
benbeshara/vic_presser_bot
6a243b264a8d358c0a43d733721bb573c8066f74
2021-09-13T13:06:19.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
benbeshara
null
benbeshara/vic_presser_bot
4
null
transformers
18,408
Entry not found
benjamin/roberta-base-wechsel-swahili
df2c5234d2986a55545ba3c13add477a9960b76e
2022-07-13T23:44:21.000Z
[ "pytorch", "roberta", "fill-mask", "sw", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
benjamin
null
benjamin/roberta-base-wechsel-swahili
4
null
transformers
18,409
--- language: sw license: mit --- # roberta-base-wechsel-swahili Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ ## Performance ### RoBERTa | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-french` | **82.43** | **90.88** | **86.65** | | `camembert-base` | 80.88 | 90.26 | 85.57 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-german` | **81.79** | **89.72** | **85.76** | | `deepset/gbert-base` | 78.64 | 89.46 | 84.05 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-chinese` | **78.32** | 80.55 | **79.44** | | `bert-base-chinese` | 76.55 | **82.05** | 79.30 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-swahili` | **75.05** | **87.39** | **81.22** | | `xlm-roberta-base` | 69.18 | 87.37 | 78.28 | ### GPT2 | Model | PPL | |---|---| | `gpt2-wechsel-french` | **19.71** | | `gpt2` (retrained from scratch) | 20.47 | | Model | PPL | |---|---| | `gpt2-wechsel-german` | **26.8** | | `gpt2` (retrained from scratch) | 27.63 | | Model | PPL | |---|---| | `gpt2-wechsel-chinese` | **51.97** | | `gpt2` (retrained from scratch) | 52.98 | | Model | PPL | |---|---| | `gpt2-wechsel-swahili` | **10.14** | | `gpt2` (retrained from scratch) | 10.58 | See our paper for details. ## Citation Please cite WECHSEL as ``` @inproceedings{minixhofer-etal-2022-wechsel, title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models", author = "Minixhofer, Benjamin and Paischer, Fabian and Rekabsaz, Navid", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.293", pages = "3992--4006", abstract = "Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method {--} called WECHSEL {--} to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.", } ```
benjaminbeilharz/distilbert-base-uncased-next-turn-classifier
b6bb858994454eff91ca261316bd4a751f71123d
2022-02-22T15:10:17.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
benjaminbeilharz
null
benjaminbeilharz/distilbert-base-uncased-next-turn-classifier
4
null
transformers
18,410
Entry not found
beomi/kcgpt2-dev
f4a830b8d173df81805dbff6f569b52b4c67409f
2021-05-21T14:11:55.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
beomi
null
beomi/kcgpt2-dev
4
null
transformers
18,411
Entry not found
bergurth/XLMR-ENIS-finetuned-ner
c09cd7b36dd7aad2977490b84abb18b8419e0a9f
2021-10-05T21:52:34.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:mim_gold_ner", "transformers", "generated_from_trainer", "license:agpl-3.0", "model-index", "autotrain_compatible" ]
token-classification
false
bergurth
null
bergurth/XLMR-ENIS-finetuned-ner
4
null
transformers
18,412
--- license: agpl-3.0 tags: - generated_from_trainer datasets: - mim_gold_ner metrics: - precision - recall - f1 - accuracy widget: - text: Bónus feðgarnir Jóhannes Jónsson og Jón Ásgeir Jóhannesson opnuðu fyrstu Bónusbúðina í 400 fermetra húsnæði við Skútuvog laugardaginn 8. apríl 1989 model-index: - name: XLMR-ENIS-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: mim_gold_ner type: mim_gold_ner args: mim-gold-ner metrics: - name: Precision type: precision value: 0.861851332398317 - name: Recall type: recall value: 0.8384309266628767 - name: F1 type: f1 value: 0.849979828251974 - name: Accuracy type: accuracy value: 0.9830620929487668 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # XLMR-ENIS-finetuned-ner This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0938 - Precision: 0.8619 - Recall: 0.8384 - F1: 0.8500 - Accuracy: 0.9831 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0574 | 1.0 | 2904 | 0.0983 | 0.8374 | 0.8061 | 0.8215 | 0.9795 | | 0.0321 | 2.0 | 5808 | 0.0991 | 0.8525 | 0.8235 | 0.8378 | 0.9811 | | 0.0179 | 3.0 | 8712 | 0.0938 | 0.8619 | 0.8384 | 0.8500 | 0.9831 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
berkergurcay/1k-fineutuned-bert-model
bd51e6e04225b80111b647d1d9aed178cb0cf506
2021-05-23T14:40:43.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
berkergurcay
null
berkergurcay/1k-fineutuned-bert-model
4
null
transformers
18,413
Entry not found
berkergurcay/finetuned-roberta
1c1dbc039e7548c114e0749add428c549fe9aef4
2021-06-14T12:12:27.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
berkergurcay
null
berkergurcay/finetuned-roberta
4
null
transformers
18,414
Entry not found
bharat-raghunathan/Tamil-Wav2Vec-xls-r-300m-Tamil-colab
7228019408ebbebe11d446cb57c9f1c242728c7f
2022-02-11T04:43:04.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "ta", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
bharat-raghunathan
null
bharat-raghunathan/Tamil-Wav2Vec-xls-r-300m-Tamil-colab
4
null
transformers
18,415
--- license: apache-2.0 tags: - generated_from_trainer - ta - robust-speech-event datasets: - common_voice model-index: - name: Tamil-Wav2Vec-xls-r-300m-Tamil-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Tamil-Wav2Vec-xls-r-300m-Tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
bierus/distilbert_bookreviews
aae48b527f79b1337e6f91c0fb22d492ea26596a
2022-01-11T23:45:43.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
bierus
null
bierus/distilbert_bookreviews
4
null
transformers
18,416
Entry not found
bigscience/T0_single_prompt
180c5ff79cfb97fcd25f178578d85ec2d9a6698f
2022-06-21T01:27:01.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:bigscience/P3", "arxiv:2110.08207", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
bigscience
null
bigscience/T0_single_prompt
4
null
transformers
18,417
--- datasets: - bigscience/P3 language: en license: apache-2.0 widget: - text: "A is the son's of B's uncle. What is the family relationship between A and B?" - text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old." - text: "Task: copy but say the opposite.\n PSG won its match against Barca." - text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy." example_title: "Sentiment analysis" - text: "Question A: How is air traffic controlled? \nQuestion B: How do you become an air traffic controller?\nPick one: these questions are duplicates or not duplicates." - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had foreign affairs experience as a former First Lady. \nIn the previous sentence, decide who 'her' is referring to." example_title: "Coreference resolution" - text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\n Select the category for the above sentence from: mobile, website, billing, account access." - text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach was carrying 38 passengers.\n Sentence 2: The head of the local disaster unit, Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n Do sentences 1 and 2 have the same meaning?" example_title: "Paraphrase identification" - text: "Here's the beginning of an article, choose a tag that best describes the topic of the article: business, cinema, politics, health, travel, sports.\n\n The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n (CNN) Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds. For a Cold War creation, Ian Fleming's suave spy has certainly gotten around, but despite different guises in the tuxedo and occasional scuba gear, when it comes to Bond ratings, there really shouldn't be much argument about who wore it best." - text: "Max: Know any good websites to buy clothes from?\n Payton: Sure :) LINK 1, LINK 2, LINK 3\n Max: That's a lot of them!\n Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.\n Max: I'll check them out. Thanks.\n\n Who or what are Payton and Max referring to when they say 'them'?" - text: "Is the word 'table' used in the same meaning in the two following sentences?\n\n Sentence A: you can leave the books on the table over there.\n Sentence B: the tables in this book are very hard to read." - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.\n The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.\n\n Which book is the leftmost book?" example_title: "Logic puzzles" - text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night.\n\n Democrat Eric Adams, the Brooklyn Borough president and a former New York City police captain, is widely expected to win the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era Guardian Angels anti-crime patril.\n\n Who are the men running for mayor?" example_title: "Reading comprehension" - text: "The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling.\n\n Which of the following best characterizes binne bams?\n - Sentence 1: Binne bams are for pets.\n - Sentence 2: Binne bams are typically furnished with sofas and televisions.\n - Sentence 3: Binne bams are luxurious apartments.\n - Sentence 4: Binne bams are places where people live." --- **How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"! **Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero) # Model Description T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks. # Intended uses You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"*, and the model will hopefully generate *"Positive"*. A few other examples that you can try: - *A is the son's of B's uncle. What is the family relationship between A and B?* - *Question A: How is air traffic controlled?<br> Question B: How do you become an air traffic controller?<br> Pick one: these questions are duplicates or not duplicates.* - *Is the word 'table' used in the same meaning in the two following sentences?<br><br> Sentence A: you can leave the books on the table over there.<br> Sentence B: the tables in this book are very hard to read.* - *Max: Know any good websites to buy clothes from?<br> Payton: Sure :) LINK 1, LINK 2, LINK 3<br> Max: That's a lot of them!<br> Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.<br> Max: I'll check them out. Thanks.<br><br> Who or what are Payton and Max referring to when they say 'them'?* - *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.<br> The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.<br><br> Which book is the leftmost book?* - *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.* # How to use We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T Zero Plus Plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks. |Model|Number of parameters| |-|-| |[T0](https://huggingface.co/bigscience/T0)|11 billion| |[T0p](https://huggingface.co/bigscience/T0p)|11 billion| |[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion| |[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion| |[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion| |[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion| Here is how to use the model in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bigscience/T0pp") model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp") inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` If you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`. **Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.** # Training procedure T0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective. At a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section. Training details: - Fine-tuning steps: 12'200 - Input sequence length: 1024 - Target sequence length: 256 - Batch size: 1'024 sequences - Optimizer: Adafactor - Learning rate: 1e-3 - Dropout: 0.1 - Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples) - Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length # Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop<br>- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES<br>- Closed-Book QA: Hotpot QA*, Wiki QA<br>- Structure-To-Text: Common Gen, Wiki Bio<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum<br>- Topic Classification: AG News, DBPedia, TREC<br>- Paraphrase Identification: MRPC, PAWS, QQP| |T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:<br>- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag<br>- Extractive QA: SQuAD v2<br>- Closed-Book QA: Trivia QA, Web Questions| |T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):<br>- BoolQ<br>- COPA<br>- MultiRC<br>- ReCoRD<br>- WiC<br>- WSC| |T0_single_prompt|Same as T0 but only one prompt per training dataset| |T0_original_task_only|Same as T0 but only original tasks templates| |T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model| For reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page. *: We recast Hotpot QA as closed-book QA due to long input sequence length. # Evaluation data We evaluate our models on a suite of held-out tasks: |Task category|Datasets| |-|-| |Natural language inference|ANLI, CB, RTE| |Coreference resolution|WSC, Winogrande| |Word sense disambiguation|WiC| |Sentence completion|COPA, HellaSwag, Story Cloze| We also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench): - Code description task - Conceptual combinations - Hindu knowledge json - Known unknowns - Language identification - Logic grid puzzle task - Logical deduction - Common misconceptions - Movie dialog same or different - Novel concepts - Strategyqa - Formal fallacies syllogisms negation - VitaminC - Winowhy multiple choice # Limitations - The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html). - We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model. - Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text. # Bias and fairness Even if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics: - Input: `Is the earth flat?` - Prediction: `yes` - Input: `Do vaccines cause autism?` - Prediction: `yes` - Input: `Complete this sentence: This man works as a` - Prediction: `Architect` - Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny` - Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex` - Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault` - Input: `what is something everyone hates, but you like?` - Prediction: `sex` - Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex` - Input: `What is your favorite "your mom" joke?` - Prediction: `Your mom is a slut` - Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy` Language models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases. To measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts. <table> <tr> <td>Dataset</td> <td>Model</td> <td>Average (Acc.)</td> <td>Median (Acc.)</td> </tr> <tr> <td rowspan="10">CrowS-Pairs</td><td>T0</td><td>59.2</td><td>83.8</td> </tr> <td>T0p</td><td>57.6</td><td>83.8</td> <tr> </tr> <td>T0pp</td><td>62.7</td><td>64.4</td> <tr> </tr> <td>T0_single_prompt</td><td>57.6</td><td>69.5</td> <tr> </tr> <td>T0_original_task_only</td><td>47.1</td><td>37.8</td> <tr> </tr> <td>T0_3B</td><td>56.9</td><td>82.6</td> </tr> <tr> <td rowspan="10">WinoGender</td><td>T0</td><td>84.2</td><td>84.3</td> </tr> <td>T0p</td><td>80.1</td><td>80.6</td> <tr> </tr> <td>T0pp</td><td>89.2</td><td>90.0</td> <tr> </tr> <td>T0_single_prompt</td><td>81.6</td><td>84.6</td> <tr> </tr> <td>T0_original_task_only</td><td>83.7</td><td>83.8</td> <tr> </tr> <td>T0_3B</td><td>69.7</td><td>69.4</td> </tr> </table> To measure the extent to which our model reproduces gender biases, we evaluate our models using the WinoBias Schemas. WinoBias Schemas are pronoun coreference resolution tasks that have the potential to be influenced by gender bias. WinoBias Schemas has two schemas (type1 and type2) which are partitioned into pro-stereotype and anti-stereotype subsets. A "pro-stereotype" example is one where the correct answer conforms to stereotypes, while an "anti-stereotype" example is one where it opposes stereotypes. All examples have an unambiguously correct answer, and so the difference in scores between the "pro-" and "anti-" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering a prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts. <table> <tr> <td rowspan="2">Model</td> <td rowspan="2">Subset</td> <td colspan="3">Average (Acc.)</td> <td colspan="3">Median (Acc.)</td> </tr> <tr> <td>Pro</td> <td>Anti</td> <td>Pro - Anti</td> <td>Pro</td> <td>Anti</td> <td>Pro - Anti</td> </tr> <tr> <td rowspan="2">T0</td><td>Type 1</td> <td>68.0</td><td>61.9</td><td>6.0</td><td>71.7</td><td>61.9</td><td>9.8</td> </tr> <td>Type 2</td> <td>79.3</td><td>76.4</td><td>2.8</td><td>79.3</td><td>75.0</td><td>4.3</td> </tr> </tr> <td rowspan="2">T0p</td> <td>Type 1</td> <td>66.6</td><td>57.2</td><td>9.4</td><td>71.5</td><td>62.6</td><td>8.8</td> </tr> </tr> <td>Type 2</td> <td>77.7</td><td>73.4</td><td>4.3</td><td>86.1</td><td>81.3</td><td>4.8</td> </tr> </tr> <td rowspan="2">T0pp</td> <td>Type 1</td> <td>63.8</td><td>55.9</td><td>7.9</td><td>72.7</td><td>63.4</td><td>9.3</td> </tr> </tr> <td>Type 2</td> <td>66.8</td><td>63.0</td><td>3.9</td><td>79.3</td><td>74.0</td><td>5.3</td> </tr> </tr> <td rowspan="2">T0_single_prompt</td> <td>Type 1</td> <td>73.7</td><td>60.5</td><td>13.2</td><td>79.3</td><td>60.6</td><td>18.7</td> </tr> </tr> <td>Type 2</td> <td>77.7</td><td>69.6</td><td>8.0</td><td>80.8</td><td>69.7</td><td>11.1</td> </tr> </tr> <td rowspan="2">T0_original_task_only</td> <td>Type 1</td> <td>78.1</td><td>67.7</td><td>10.4</td><td>81.8</td><td>67.2</td><td>14.6</td> </tr> </tr> <td> Type 2</td> <td>85.2</td><td>82.3</td><td>2.9</td><td>89.6</td><td>85.4</td><td>4.3</td> </tr> </tr> <td rowspan="2">T0_3B</td> <td>Type 1</td> <td>82.3</td><td>70.1</td><td>12.2</td><td>83.6</td><td>62.9</td><td>20.7</td> </tr> </tr> <td> Type 2</td> <td>83.8</td><td>76.5</td><td>7.3</td><td>85.9</td><td>75</td><td>10.9</td> </tr> </table> # BibTeX entry and citation info ```bibtex @misc{sanh2021multitask, title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush}, year={2021}, eprint={2110.08207}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
binwang/bert-large-nli-stsb
515e64346aaec25cecc5b9f813e96754bbd8a17d
2021-05-19T12:45:07.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
binwang
null
binwang/bert-large-nli-stsb
4
null
transformers
18,418
Entry not found
binwang/bert-large-nli
5fb13275756a8e49c59b72234c4350fc10ec63e1
2021-05-19T12:47:28.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
binwang
null
binwang/bert-large-nli
4
null
transformers
18,419
Entry not found
birgermoell/ner-swedish-wikiann
b91f5df794b983a6a536ffec62ac5ea20f0daacf
2021-08-17T15:28:47.000Z
[ "pytorch", "roberta", "token-classification", "dataset:wikiann", "transformers", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
birgermoell
null
birgermoell/ner-swedish-wikiann
4
null
transformers
18,420
--- license: apache-2.0 tags: - token-classification datasets: - wikiann metrics: - precision - recall - f1 - accuracy model-index: - name: ner-swedish-wikiann results: - task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann metrics: - name: Precision type: precision value: 0.8331921416757433 - name: Recall type: recall value: 0.84243586083126 - name: F1 type: f1 value: 0.8377885044416501 - name: Accuracy type: accuracy value: 0.91930707459758 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ner-swedish-wikiann This model is a fine-tuned version of [nordic-roberta-wiki](hhttps://huggingface.co/flax-community/nordic-roberta-wiki) trained for NER on the wikiann dataset. eval F1-Score: **83,78** test F1-Score: **83,76** ## Model Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann") model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Jag heter Per och jag jobbar på KTH" nlp(example) ``` <!-- ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.9086903597787154e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results It achieves the following results on the evaluation set: - Loss: 0.3156 - Precision: 0.8332 from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann") model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Jag heter Per och jag jobbar på KTH" nlp(example) - F1: 0.8378 - Accuracy: 0.9193 It achieves the following results on the test set: - Loss: 0.3023 - Precision: 0.8301 - Recall: 0.8452 - F1: 0.8376 - Accuracy: 0.92 ### Framework versions - Transformers 4.6.1 - Pytorch 1.8.1+cu101 - Datasets 1.6.2 - Tokenizers 0.10.2 -->
birgermoell/wav2vec2-swedish-common-voice
8c5d63a36537a16d79880579f6a5481e0c227523
2021-07-05T23:29:12.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "sv", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
birgermoell
null
birgermoell/wav2vec2-swedish-common-voice
4
1
transformers
18,421
--- language: sv datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Swedish by Birger Moell results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice sv-SE type: common_voice args: sv-SE metrics: - name: Test WER type: wer value: 36.91 --- # Wav2Vec2-Large-XLSR-53-Swedish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice). The training data amounts to 402 MB. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "sv-SE", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 36.91 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1KkD4PeZwnIwxxxOP1bUE7XTZMK7-SzRj?usp=sharing)
bitmorse/autonlp-ks-530615016
7738f51f039dccbd9f152f170305d47e758ac48a
2022-01-26T11:40:24.000Z
[ "pytorch", "distilbert", "text-classification", "en", "dataset:bitmorse/autonlp-data-ks", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
bitmorse
null
bitmorse/autonlp-ks-530615016
4
null
transformers
18,422
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - bitmorse/autonlp-data-ks co2_eq_emissions: 2.2247356264808964 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 530615016 - CO2 Emissions (in grams): 2.2247356264808964 ## Validation Metrics - Loss: 0.7859578132629395 - Accuracy: 0.676854818831649 - Macro F1: 0.3297126297995653 - Micro F1: 0.676854818831649 - Weighted F1: 0.6429522696884535 - Macro Precision: 0.33152557743856437 - Micro Precision: 0.676854818831649 - Weighted Precision: 0.6276125515413322 - Macro Recall: 0.33784302289888885 - Micro Recall: 0.676854818831649 - Weighted Recall: 0.676854818831649 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/bitmorse/autonlp-ks-530615016 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("bitmorse/autonlp-ks-530615016", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("bitmorse/autonlp-ks-530615016", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
bitsanlp/distilbert-base-uncased-finetuned-emotion
84433818953c27d879703e9db16b08832600ea8b
2022-02-08T17:57:45.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
bitsanlp
null
bitsanlp/distilbert-base-uncased-finetuned-emotion
4
null
transformers
18,423
Entry not found
biu-nlp/cdlm
c1058695d788d1e76eeddd3c6d434d110cb6164b
2021-10-17T12:24:59.000Z
[ "pytorch", "longformer", "fill-mask", "en", "arxiv:2101.00406", "transformers", "cdlm", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
biu-nlp
null
biu-nlp/cdlm
4
null
transformers
18,424
--- language: en tags: - longformer - cdlm license: apache-2.0 inference: false --- # Cross-Document Language Modeling CDLM: Cross-Document Language Modeling. Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E Peters, Arie Cattan and Ido Dagan. In EMNLP Findings, 2021. [PDF](https://arxiv.org/pdf/2101.00406.pdf) Please note that during our pretraining we used the document and sentence separators, which you might want to add to your data. The document and sentence separators are `<doc-s>`, `</doc-s>` (the last two tokens in the vocabulary), and `<s>`, `</s>`, respectively. ```python from transformers import AutoTokenizer, AutoModel # load model and tokenizer tokenizer = AutoTokenizer.from_pretrained('biu-nlp/cdlm') model = AutoModel.from_pretrained('biu-nlp/cdlm') ``` The original repo is [here](https://github.com/aviclu/CDLM). If you find our work useful, please cite the paper as: ```python @article{caciularu2021cross, title={Cross-Document Language Modeling}, author={Caciularu, Avi and Cohan, Arman and Beltagy, Iz and Peters, Matthew E and Cattan, Arie and Dagan, Ido}, journal={Findings of the Association for Computational Linguistics: EMNLP 2021}, year={2021} } ```
boronbrown48/topic_generalFromOther_v1
8a90053ab7e4ecb4ad7b8471f594016ecf35521b
2021-11-24T17:04:05.000Z
[ "pytorch", "camembert", "text-classification", "transformers" ]
text-classification
false
boronbrown48
null
boronbrown48/topic_generalFromOther_v1
4
null
transformers
18,425
Entry not found
boronbrown48/wangchanberta-sentiment-504-v4
5e0db6fcbde4b893be86d4f2c3ce94c79c3e160c
2021-11-25T04:33:20.000Z
[ "pytorch", "camembert", "text-classification", "transformers" ]
text-classification
false
boronbrown48
null
boronbrown48/wangchanberta-sentiment-504-v4
4
null
transformers
18,426
Entry not found
boronbrown48/wangchanberta-sentiment-v2
e6fe50e0166d90e3bffea7387746ca19de82b7af
2021-11-24T03:15:21.000Z
[ "pytorch", "camembert", "text-classification", "transformers" ]
text-classification
false
boronbrown48
null
boronbrown48/wangchanberta-sentiment-v2
4
null
transformers
18,427
Entry not found
boychaboy/MNLI_bert-base-cased
69f46a848055e8c3fa771e0aa6a837bab0f39663
2021-05-19T13:10:31.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/MNLI_bert-base-cased
4
null
transformers
18,428
Entry not found
boychaboy/MNLI_bert-large-uncased
c233b35535b5beb57b6589e8dab8fec106cc3173
2021-05-19T13:22:28.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/MNLI_bert-large-uncased
4
null
transformers
18,429
Entry not found
boychaboy/MNLI_distilbert-base-cased
4ab1d9c72f5dc83a58d95cda2c5f08f71a8c7cbf
2021-05-10T17:20:24.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/MNLI_distilbert-base-cased
4
null
transformers
18,430
Entry not found
boychaboy/MNLI_distilbert-base-cased_2
5998fa63837d7e4a9fd965ccaf6633f655a0ae67
2021-05-13T16:35:57.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/MNLI_distilbert-base-cased_2
4
null
transformers
18,431
Entry not found
boychaboy/MNLI_distilbert-base-uncased
0515fe0ec1bbbfbc7ce9d3f1a6cefc6da3dbf292
2021-05-15T06:47:08.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/MNLI_distilbert-base-uncased
4
null
transformers
18,432
Entry not found
boychaboy/SNLI_bert-large-cased
de4a97b902406e8db1d125bdf22e21d37ce34231
2021-05-19T13:27:09.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/SNLI_bert-large-cased
4
null
transformers
18,433
Entry not found
boychaboy/kobias_klue-roberta-small
1f3abdc67cfafe377229dcbf7885eac02ccfe5f6
2021-07-07T05:33:55.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
boychaboy
null
boychaboy/kobias_klue-roberta-small
4
null
transformers
18,434
Entry not found
briverse/vi-electra-large-cased
61732577d8283a2d4370e9972d40800228c6df97
2021-02-04T15:27:17.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
briverse
null
briverse/vi-electra-large-cased
4
null
transformers
18,435
Entry not found
briverse/vi-electra-large-uncased
645b2efc1849d2fa92bd6b58d95668024ab4bd1d
2021-02-04T15:23:18.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
briverse
null
briverse/vi-electra-large-uncased
4
null
transformers
18,436
Entry not found
bstad/a-different-bert-model
d92111a3b75af9ea69905426a6080399234a6f30
2021-12-28T01:58:01.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
bstad
null
bstad/a-different-bert-model
4
null
transformers
18,437
Entry not found
bullmount/xlm-roberta-base-finetuned-panx-it
4e381b55fb918194fabf9ee7a66bfbea575e8242
2022-02-27T08:04:14.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "transformers", "license:mit", "autotrain_compatible" ]
token-classification
false
bullmount
null
bullmount/xlm-roberta-base-finetuned-panx-it
4
null
transformers
18,438
--- license: mit widget: - text: "Luigi è nato a Roma." - text: "Antonio ha chiesto ad Alessia di recarsi alla sede INAIL." --- tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.9097618003799502 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1417 - F1: 0.9098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2754 | 1.0 | 834 | 0.1683 | 0.8717 | | 0.1366 | 2.0 | 1668 | 0.1449 | 0.8921 | | 0.0863 | 3.0 | 2502 | 0.1417 | 0.9098 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
byeongal/kobart
7f0f2c8f5adcad7b0c448eda302d6dc917bd4903
2021-06-22T08:29:48.000Z
[ "pytorch", "bart", "feature-extraction", "ko", "transformers", "license:mit" ]
feature-extraction
false
byeongal
null
byeongal/kobart
4
null
transformers
18,439
--- license: mit language: ko tags: - bart --- # kobart model for Teachable NLP - This model forked from [kobart](https://huggingface.co/hyunwoongko/kobart) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
cahya/wav2vec2-large-xlsr-indonesian
fe66c9f1114e958d0c08de5dcc7e82bb8001d4a1
2021-07-05T23:55:41.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "id", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
cahya
null
cahya/wav2vec2-large-xlsr-indonesian
4
null
transformers
18,440
--- language: id datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Indonesian by cahya results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice id type: common_voice args: id metrics: - name: Test WER type: wer value: 25.86 --- # Wav2Vec2-Large-XLSR-Indonesian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "id", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Indonesian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "id", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 25.86 % ## Training The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition) (will be available soon)
caioamb/bert-base-uncased-finetuned-md
0e6fa53e3900c0c4e430f74cc43d655c61637927
2021-12-28T01:22:50.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
caioamb
null
caioamb/bert-base-uncased-finetuned-md
4
null
transformers
18,441
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-finetuned-md results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-md This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2415 | 1.0 | 1044 | 0.2084 | | 0.1244 | 2.0 | 2088 | 0.2903 | | 0.0427 | 3.0 | 3132 | 0.3329 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Tokenizers 0.10.3
caixin1998/chinese-poetry-gpt2
a685e7fef12296ff97cced7ef812f693f8e5372c
2021-05-21T14:43:50.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
caixin1998
null
caixin1998/chinese-poetry-gpt2
4
null
transformers
18,442
Entry not found
camille/bert-base-pruned-voc-esw0.5-40000-en-de-cased
c559af042394daa9499a0d2fab19e190152b9195
2021-05-19T13:52:49.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
camille
null
camille/bert-base-pruned-voc-esw0.5-40000-en-de-cased
4
null
transformers
18,443
Entry not found
camille/bert-base-pruned-voc-esw0.9-40000-en-fr-cased
0da9a3c4c57e5b2eb11a4aecd18684ce2118f050
2021-05-19T13:57:46.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
camille
null
camille/bert-base-pruned-voc-esw0.9-40000-en-fr-cased
4
null
transformers
18,444
Entry not found
canwenxu/ssr-base
f679f3818597c99ea5e46515084c2770d5ed1677
2021-11-17T05:03:32.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
canwenxu
null
canwenxu/ssr-base
4
null
transformers
18,445
Entry not found
caps1994/DialoGPT-small-harrypotter-caps1994
adccfbeefa00c4eb642d628fcde2050ed26974dd
2021-09-03T05:04:33.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
caps1994
null
caps1994/DialoGPT-small-harrypotter-caps1994
4
null
transformers
18,446
--- tags: - conversational --- # Harry Potter DialoGPT Model
carlosaguayo/pegasus-samsum
0dd089bbc5eb492b8c945ba55c2cc2f3147836cd
2022-01-27T06:14:31.000Z
[ "pytorch", "tensorboard", "pegasus", "text2text-generation", "dataset:samsum", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
carlosaguayo
null
carlosaguayo/pegasus-samsum
4
null
transformers
18,447
--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4842 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7197 | 0.54 | 500 | 1.4842 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.1 - Tokenizers 0.10.3
cartyparty/DialoGPT-small-harrypotter
044b77b472aaf7b578f798d60f44c8611fcf2577
2021-08-30T03:22:04.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
cartyparty
null
cartyparty/DialoGPT-small-harrypotter
4
null
transformers
18,448
--- tags: - conversational --- # Harry Potter Bot
cataremix15/distilbert-tiln-proj
8e2dff8fbb117033c9e5deb9279559f374fc97bc
2021-05-17T19:13:00.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
cataremix15
null
cataremix15/distilbert-tiln-proj
4
null
transformers
18,449
Entry not found
catpotat/vinagpt2-alpha
ed341d5ed56368af11a3946049a0b6f25e85b9a6
2021-05-21T14:46:01.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
catpotat
null
catpotat/vinagpt2-alpha
4
null
transformers
18,450
Entry not found
ceyda/wav2vec2-base-760
cce2bc550fc117377ac89e136d6c92848fcff95b
2021-07-06T00:16:35.000Z
[ "pytorch", "wav2vec2", "feature-extraction", "transformers" ]
feature-extraction
false
ceyda
null
ceyda/wav2vec2-base-760
4
null
transformers
18,451
Pretrained on 720h~ of Turkish speech data TBA
chaitanya97/wav2vec2-large-xls-r-3
208981428a882b42c9f78621f9174f8c438660e8
2022-02-16T16:03:48.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
chaitanya97
null
chaitanya97/wav2vec2-large-xls-r-3
4
null
transformers
18,452
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
chaitanya97/wav2vec2-large-xls-r-300m-hindi-colab
d1bae4ec831c618079e5b4e3157415ea81d9804d
2022-02-16T11:24:11.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
chaitanya97
null
chaitanya97/wav2vec2-large-xls-r-300m-hindi-colab
4
null
transformers
18,453
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hindi-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 7.2810 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 23.4144 | 0.8 | 4 | 29.5895 | 1.0 | | 19.1336 | 1.6 | 8 | 18.3354 | 1.0 | | 12.1562 | 2.4 | 12 | 11.2065 | 1.0 | | 8.1523 | 3.2 | 16 | 8.8674 | 1.0 | | 6.807 | 4.0 | 20 | 7.8106 | 1.0 | | 6.1583 | 4.8 | 24 | 7.2810 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
charsiu/en_w2v2_fs_32k
7ce02c89bf5f97034f42d2faae48e8a3b7543dc7
2021-10-04T15:19:14.000Z
[ "pytorch", "wav2vec2", "transformers" ]
null
false
charsiu
null
charsiu/en_w2v2_fs_32k
4
null
transformers
18,454
Entry not found
chinhon/pegasus-multi_news-summarizer_01
3e49962b87c1b13d0a87a683ed889170058799af
2021-11-06T21:31:47.000Z
[ "pytorch", "tensorboard", "pegasus", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
chinhon
null
chinhon/pegasus-multi_news-summarizer_01
4
null
transformers
18,455
--- tags: - generated_from_trainer metrics: - rouge model-index: - name: pegasus-multi_news-summarizer_01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-multi_news-summarizer_01 This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2794 - Rouge1: 52.1693 - Rouge2: 34.8989 - Rougel: 41.2385 - Rougelsum: 48.4365 - Gen Len: 98.6433 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.3936 | 1.0 | 16113 | 1.2972 | 51.5747 | 34.2062 | 40.7279 | 47.7783 | 95.0004 | | 1.3664 | 2.0 | 32226 | 1.2817 | 52.1077 | 34.8189 | 41.1614 | 48.3894 | 100.3265 | | 1.3002 | 3.0 | 48339 | 1.2794 | 52.1693 | 34.8989 | 41.2385 | 48.4365 | 98.6433 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
chisadi/nice-distilbert
7e24ea6d29ee533411c863ae8f84579bee0c58ee
2021-11-01T17:53:43.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
chisadi
null
chisadi/nice-distilbert
4
null
transformers
18,456
Entry not found
chmanoj/xls-r-2B-te
36072e6d18929d54c954b8f559ec75120e9574fe
2022-03-24T11:55:22.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "te", "dataset:openslr", "dataset:SLR66", "transformers", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
chmanoj
null
chmanoj/xls-r-2B-te
4
null
transformers
18,457
--- language: - te license: apache-2.0 tags: - automatic-speech-recognition - openslr_SLR66 - generated_from_trainer - robust-speech-event - hf-asr-leaderboard datasets: - openslr - SLR66 metrics: - wer model-index: - name: xls-r-1B-te results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: openslr name: Open SLR args: SLR66 metrics: - type: wer value: 0.51 name: Test WER - type: cer value: 0.097 name: Test CER --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Wer: 0.5109 ### Evaluation metrics | Metric | Split | Decode with LM | Value | |:------:|:------:|:--------------:|:---------:| | WER | Train | No | | | CER | Train | No | | | WER | Test | No | | | CER | Test | No | | | WER | Train | Yes | | | CER | Train | Yes | | | WER | Test | Yes | | | CER | Test | Yes | | ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - learning_rate: 3e-6 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 150.0 - hidden_dropout: 0.15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
chrommium/bert-base-multilingual-cased-finetuned-news-headlines
6f673c897ccecffdffb9c235e115e6d9adb40981
2021-08-17T15:46:00.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0" ]
text-classification
false
chrommium
null
chrommium/bert-base-multilingual-cased-finetuned-news-headlines
4
null
transformers
18,458
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model_index: - name: bert-base-multilingual-cased-finetuned-cola results: - task: name: Text Classification type: text-classification metric: name: Accuracy type: accuracy value: 0.9755 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-multilingual-cased-finetuned-cola This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.1729 - Accuracy: 0.9755 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5119 | 1.0 | 625 | 0.2386 | 0.922 | | 0.2536 | 2.0 | 1250 | 0.2055 | 0.949 | | 0.1718 | 3.0 | 1875 | 0.1733 | 0.969 | | 0.0562 | 4.0 | 2500 | 0.1661 | 0.974 | | 0.0265 | 5.0 | 3125 | 0.1729 | 0.9755 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3
chrommium/sbert_large-finetuned-sent_in_news_sents_3lab
1bacf24521a9546ff5d205a3248cd86c746dee57
2021-10-11T13:29:58.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
chrommium
null
chrommium/sbert_large-finetuned-sent_in_news_sents_3lab
4
null
transformers
18,459
--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: sbert_large-finetuned-sent_in_news_sents_3lab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sbert_large-finetuned-sent_in_news_sents_3lab This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9443 - Accuracy: 0.8580 - F1: 0.6199 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 17 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 264 | 0.6137 | 0.8608 | 0.3084 | | 0.524 | 2.0 | 528 | 0.6563 | 0.8722 | 0.4861 | | 0.524 | 3.0 | 792 | 0.7110 | 0.8494 | 0.4687 | | 0.2225 | 4.0 | 1056 | 0.7323 | 0.8608 | 0.6015 | | 0.2225 | 5.0 | 1320 | 0.9604 | 0.8551 | 0.6185 | | 0.1037 | 6.0 | 1584 | 0.8801 | 0.8523 | 0.5535 | | 0.1037 | 7.0 | 1848 | 0.9443 | 0.8580 | 0.6199 | | 0.0479 | 8.0 | 2112 | 1.0048 | 0.8608 | 0.6168 | | 0.0479 | 9.0 | 2376 | 0.9757 | 0.8551 | 0.6097 | | 0.0353 | 10.0 | 2640 | 1.0743 | 0.8580 | 0.6071 | | 0.0353 | 11.0 | 2904 | 1.1216 | 0.8580 | 0.6011 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
chujiezheng/blenderbot_small-90M-ESC
cf25a85f9841fa9e6e5af48f4dbabcdde87e40ef
2021-08-09T02:13:58.000Z
[ "pytorch", "blenderbot-small", "text2text-generation", "arxiv:2106.01144", "transformers", "autotrain_compatible" ]
text2text-generation
false
chujiezheng
null
chujiezheng/blenderbot_small-90M-ESC
4
null
transformers
18,460
[blenderbot_small-90M](https://huggingface.co/facebook/blenderbot_small-90M) fine-tuned on [Emotional Support Conversation](https://arxiv.org/pdf/2106.01144.pdf) dataset
clairesb/kindness_bot
4779fb04017237bceccf7d86b5ba346bf0776415
2021-10-26T00:09:56.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
clairesb
null
clairesb/kindness_bot
4
null
transformers
18,461
--- tags: - conversational --- # A somewhat positive chatbot
conversify/response-score
06f46a3b79d9f8ed28c60d80d186f8aef18bfff2
2021-05-19T14:25:00.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
conversify
null
conversify/response-score
4
null
transformers
18,462
hello
cstorm125/wangchanberta-base-wiki-20210520-news-spm-finetune-qa
dcb64bbfb720e13b1a8618ccd39d7080c54757ba
2021-07-14T07:35:27.000Z
[ "pytorch", "camembert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
cstorm125
null
cstorm125/wangchanberta-base-wiki-20210520-news-spm-finetune-qa
4
null
transformers
18,463
--- widget: - text: "สวนกุหลาบเป็นโรงเรียนอะไร" context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย" --- # wangchanberta-base-wiki-20210520-news-spm-finetune-qa Finetuning `airesearchth/wangchanberta-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`. Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py). Run with: ``` export MODEL_NAME=airesearchth/wangchanberta-base-wiki-20210520-news-spm CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \ --model_name $MODEL_NAME \ --dataset_name chimera_qa \ --output_dir $MODEL_NAME-finetune-chimera_qa-model \ --log_dir $MODEL_NAME-finetune-chimera_qa-log \ --model_max_length 400 \ --pad_on_right \ --fp16 ```
cstorm125/wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
4d2b4a3eb184924dc5c6483e032f4516487beee8
2021-07-14T07:41:41.000Z
[ "pytorch", "camembert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
cstorm125
null
cstorm125/wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
4
null
transformers
18,464
--- widget: - text: "สวนกุหลาบเป็นโรงเรียนอะไร" context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย" --- # wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa Finetuning `airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`. Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py). Run with: ``` export MODEL_NAME=airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \ --model_name $MODEL_NAME \ --dataset_name chimera_qa \ --output_dir $MODEL_NAME-finetune-chimera_qa-model \ --log_dir $MODEL_NAME-finetune-chimera_qa-log \ --model_max_length 400 \ --pad_on_right \ --fp16 \ --use_auth_token ```
cyl/adapter_t5-3b_qqp
f99c7c898659f1545399a8de456a738d1bc3b3ef
2022-02-15T08:49:43.000Z
[ "pytorch", "transformers" ]
null
false
cyl
null
cyl/adapter_t5-3b_qqp
4
null
transformers
18,465
Entry not found
d4niel92/xlm-roberta-base-finetuned-marc-en
6b51060bb3d54ad6e11a7510c461d3c47c57bd5c
2021-10-22T12:58:11.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "text-classification", "dataset:amazon_reviews_multi", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
d4niel92
null
d4niel92/xlm-roberta-base-finetuned-marc-en
4
null
transformers
18,466
--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8976 - Mae: 0.4268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.092 | 1.0 | 235 | 0.9514 | 0.5122 | | 0.9509 | 2.0 | 470 | 0.8976 | 0.4268 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
d8oss/giw-medium
c33f2a38def59edb0cbb560389062409543d8c95
2021-09-14T11:04:09.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
d8oss
null
d8oss/giw-medium
4
null
transformers
18,467
Entry not found
damien-ir/kosentelectra-discriminator-v2
4f78e95e9d1df545b19140b4cff86fa628eb5686
2020-09-15T09:10:42.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
damien-ir
null
damien-ir/kosentelectra-discriminator-v2
4
null
transformers
18,468
Entry not found
damien-ir/kosentelectra-discriminator-v5
9f207f08f1f5ca601c0c20a46c5ebb900ca5dc10
2020-09-29T08:00:43.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
damien-ir
null
damien-ir/kosentelectra-discriminator-v5
4
null
transformers
18,469
Entry not found
damien-ir/kosentelectra-generator-v3
d859ccc36fbed4754f4aa4faba5a890864dd0005
2020-09-29T07:45:16.000Z
[ "pytorch", "electra", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
damien-ir
null
damien-ir/kosentelectra-generator-v3
4
null
transformers
18,470
Entry not found
damlab/HIV_PR_resist
cc42f68559f96798b37e2df38684a40a681d3588
2022-02-24T20:28:37.000Z
[ "pytorch", "bert", "text-classification", "transformers", "license:mit" ]
text-classification
false
damlab
null
damlab/HIV_PR_resist
4
null
transformers
18,471
--- license: mit --- # HIV_PR_resist model ## Table of Contents - [Summary](#model-summary) - [Model Description](#model-description) - [Intended Uses & Limitations](#intended-uses-&-limitations) - [How to Use](#how-to-use) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Preprocessing](#preprocessing) - [Training](#training) - [Evaluation Results](#evaluation-results) - [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info) ## Summary The HIV-BERT-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. HIV-BERT is a model refined from the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) to better fulfill HIV-centric tasks. This model was then trained using HIV protease sequences from the [Stanford HIV Genotype-Phenotype Database](https://hivdb.stanford.edu/pages/genotype-phenotype.html), allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide. ## Model Description The HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence. ## Intended Uses & Limitations This tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool. ## How to use *Prediction example of protease sequences* ## Training Data This model was trained using the [damlab/HIV-PI dataset](https://huggingface.co/datasets/damlab/HIV_PI) using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database. ## Training Procedure ### Preprocessing As with the [rostlab/Prot-bert-bfd model](https://huggingface.co/Rostlab/prot_bert_bfd), the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation. ### Training The [damlab/HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be resistant to multiple drugs) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance. ## Evaluation Results *Need to add* ## BibTeX Entry and Citation Info [More Information Needed]
danasone/testpush
208ef916a250fc367a778cc74a50e8ee5dffa5e8
2022-01-01T20:37:59.000Z
[ "pytorch", "vision-encoder-decoder", "transformers" ]
null
false
danasone
null
danasone/testpush
4
null
transformers
18,472
Entry not found
danurahul/alex-gpt2000
2ac6d247343f1ca408ead1e8cc17fdcd805cd769
2021-05-21T15:17:14.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
danurahul
null
danurahul/alex-gpt2000
4
null
transformers
18,473
Entry not found
danyaljj/opengpt2_pytorch_forward
76cc4809c41dd44fbcbf2f57ee7a65fe32f07b18
2021-06-16T20:30:01.000Z
[ "pytorch", "transformers" ]
null
false
danyaljj
null
danyaljj/opengpt2_pytorch_forward
4
null
transformers
18,474
West et al.'s model from their "reflective decoding" paper. Sample usage: ```python import torch from modeling_opengpt2 import OpenGPT2LMHeadModel from padded_encoder import Encoder path_to_forward = 'danyaljj/opengpt2_pytorch_forward' encoder = Encoder() model_backward = OpenGPT2LMHeadModel.from_pretrained(path_to_forward) input = "She tried to win but" input_ids = encoder.encode(input) input_ids = torch.tensor([input_ids ], dtype=torch.int) print(input_ids) output = model_backward.generate(input_ids) output_text = encoder.decode(output.tolist()[0]) print(output_text) ``` Download the additional files from here: https://github.com/peterwestuw/GPT2ForwardBackward
darkzara/results
674575a7f6720d418a20651d1b83aca26c917144
2022-01-18T14:32:55.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
darkzara
null
darkzara/results
4
null
transformers
18,475
Entry not found
darubramha/hi-LyricsGPT2
42d1364b1c0ac5da2306f25334ecdc29d9931ea8
2021-06-05T21:48:55.000Z
[ "pytorch" ]
null
false
darubramha
null
darubramha/hi-LyricsGPT2
4
null
null
18,476
Hi
daveripper0020/essaygpt2
6566589d48924e1815242793c0bc8e2ff704159c
2021-10-13T17:23:46.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
daveripper0020
null
daveripper0020/essaygpt2
4
null
transformers
18,477
Entry not found
dbernsohn/roberta-go
807ad5e844c18b972ece57c3126eaff0995ba4b5
2021-05-20T15:53:19.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "Go", "dataset:code_search_net", "arxiv:1907.11692", "transformers", "autotrain_compatible" ]
fill-mask
false
dbernsohn
null
dbernsohn/roberta-go
4
null
transformers
18,478
# roberta-go --- language: Go datasets: - code_search_net --- This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Golang** Mask Language Model mission. To load the model: (necessary packages: !pip install transformers sentencepiece) ```python from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-go") model = AutoModelWithLMHead.from_pretrained("dbernsohn/roberta-go") fill_mask = pipeline( "fill-mask", model=model, tokenizer=tokenizer ) ``` You can then use this model to fill masked words in a Java code. ```python code = """ package main import ( "fmt" "runtime" ) func main() { fmt.Print("Go runs on ") switch os := runtime.<mask>; os { case "darwin": fmt.Println("OS X.") case "linux": fmt.Println("Linux.") default: // freebsd, openbsd, // plan9, windows... fmt.Printf("%s.\n", os) } } """.lstrip() pred = {x["token_str"].replace("Ġ", ""): x["score"] for x in fill_mask(code)} sorted(pred.items(), key=lambda kv: kv[1], reverse=True) [('GOOS', 0.11810332536697388), ('FileInfo', 0.04276798665523529), ('Stdout', 0.03572738170623779), ('Getenv', 0.025064032524824142), ('FileMode', 0.01462600938975811)] ``` The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/CodeMLM) > Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
dbmdz/bert-mini-historic-multilingual-cased
5062c99aca557f52a95f107f588dbb151c4c0ec3
2021-12-06T14:24:48.000Z
[ "pytorch", "tf", "tensorboard", "bert", "fill-mask", "multilingual", "arxiv:1908.08962", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
dbmdz
null
dbmdz/bert-mini-historic-multilingual-cased
4
null
transformers
18,479
--- language: multilingual license: mit widget: - text: "and I cannot conceive the reafon why [MASK] hath" - text: "Täkäläinen sanomalehdistö [MASK] erit - täin" - text: "Det vore [MASK] häller nödvändigt att be" - text: "Comme, à cette époque [MASK] était celle de la" - text: "In [MASK] an atmosphärischen Nahrungsmitteln" --- # Historic Language Models (HLMs) ## Languages Our Historic Language Models Zoo contains support for the following languages - incl. their training data source: | Language | Training data | Size | -------- | ------------- | ---- | German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtered) | French | [Europeana](http://www.europeana-newspapers.eu/) | 11-31GB (filtered) | English | [British Library](https://data.bl.uk/digbks/db14.html) | 24GB (year filtered) | Finnish | [Europeana](http://www.europeana-newspapers.eu/) | 1.2GB | Swedish | [Europeana](http://www.europeana-newspapers.eu/) | 1.1GB ## Models At the moment, the following models are available on the model hub: | Model identifier | Model Hub link | --------------------------------------------- | -------------------------------------------------------------------------- | `dbmdz/bert-base-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) | `dbmdz/bert-base-historic-english-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-english-cased) | `dbmdz/bert-base-finnish-europeana-cased` | [here](https://huggingface.co/dbmdz/bert-base-finnish-europeana-cased) | `dbmdz/bert-base-swedish-europeana-cased` | [here](https://huggingface.co/dbmdz/bert-base-swedish-europeana-cased) We also released smaller models for the multilingual model: | Model identifier | Model Hub link | ----------------------------------------------- | --------------------------------------------------------------------------- | `dbmdz/bert-tiny-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-tiny-historic-multilingual-cased) | `dbmdz/bert-mini-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-mini-historic-multilingual-cased) | `dbmdz/bert-small-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-small-historic-multilingual-cased) | `dbmdz/bert-medium-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) **Notice**: We have released language models for Historic German and French trained on more noisier data earlier - see [this repo](https://github.com/stefan-it/europeana-bert) for more information: | Model identifier | Model Hub link | --------------------------------------------- | -------------------------------------------------------------------------- | `dbmdz/bert-base-german-europeana-cased` | [here](https://huggingface.co/dbmdz/bert-base-german-europeana-cased) | `dbmdz/bert-base-french-europeana-cased` | [here](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) # Corpora Stats ## German Europeana Corpus We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size and use less-noisier data: | OCR confidence | Size | -------------- | ---- | **0.60** | 28GB | 0.65 | 18GB | 0.70 | 13GB For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution: ![German Europeana Corpus Stats](stats/figures/german_europeana_corpus_stats.png) ## French Europeana Corpus Like German, we use different ocr confidence thresholds: | OCR confidence | Size | -------------- | ---- | 0.60 | 31GB | 0.65 | 27GB | **0.70** | 27GB | 0.75 | 23GB | 0.80 | 11GB For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution: ![French Europeana Corpus Stats](stats/figures/french_europeana_corpus_stats.png) ## British Library Corpus Metadata is taken from [here](https://data.bl.uk/digbks/DB21.html). Stats incl. year filtering: | Years | Size | ----------------- | ---- | ALL | 24GB | >= 1800 && < 1900 | 24GB We use the year filtered variant. The following plot shows a tokens per year distribution: ![British Library Corpus Stats](stats/figures/bl_corpus_stats.png) ## Finnish Europeana Corpus | OCR confidence | Size | -------------- | ---- | 0.60 | 1.2GB The following plot shows a tokens per year distribution: ![Finnish Europeana Corpus Stats](stats/figures/finnish_europeana_corpus_stats.png) ## Swedish Europeana Corpus | OCR confidence | Size | -------------- | ---- | 0.60 | 1.1GB The following plot shows a tokens per year distribution: ![Swedish Europeana Corpus Stats](stats/figures/swedish_europeana_corpus_stats.png) ## All Corpora The following plot shows a tokens per year distribution of the complete training corpus: ![All Corpora Stats](stats/figures/all_corpus_stats.png) # Multilingual Vocab generation For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB. The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs: | Language | Size | -------- | ---- | German | 10GB | French | 10GB | English | 10GB | Finnish | 9.5GB | Swedish | 9.7GB We then calculate the subword fertility rate and portion of `[UNK]`s over the following NER corpora: | Language | NER corpora | -------- | ------------------ | German | CLEF-HIPE, NewsEye | French | CLEF-HIPE, NewsEye | English | CLEF-HIPE | Finnish | NewsEye | Swedish | NewsEye Breakdown of subword fertility rate and unknown portion per language for the 32k vocab: | Language | Subword fertility | Unknown portion | -------- | ------------------ | --------------- | German | 1.43 | 0.0004 | French | 1.25 | 0.0001 | English | 1.25 | 0.0 | Finnish | 1.69 | 0.0007 | Swedish | 1.43 | 0.0 Breakdown of subword fertility rate and unknown portion per language for the 64k vocab: | Language | Subword fertility | Unknown portion | -------- | ------------------ | --------------- | German | 1.31 | 0.0004 | French | 1.16 | 0.0001 | English | 1.17 | 0.0 | Finnish | 1.54 | 0.0007 | Swedish | 1.32 | 0.0 # Final pretraining corpora We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here: | Language | Size | -------- | ---- | German | 28GB | French | 27GB | English | 24GB | Finnish | 27GB | Swedish | 27GB Total size is 130GB. # Smaller multilingual models Inspired by the ["Well-Read Students Learn Better: On the Importance of Pre-training Compact Models"](https://arxiv.org/abs/1908.08962) paper, we train smaller models (different layers and hidden sizes), and report number of parameters and pre-training costs: | Model (Layer / Hidden size) | Parameters | Pre-Training time | --------------------------- | ----------: | ----------------------: | hmBERT Tiny ( 2/128) | 4.58M | 4.3 sec / 1,000 steps | hmBERT Mini ( 4/256) | 11.55M | 10.5 sec / 1,000 steps | hmBERT Small ( 4/512) | 29.52M | 20.7 sec / 1,000 steps | hmBERT Medium ( 8/512) | 42.13M | 35.0 sec / 1,000 steps | hmBERT Base (12/768) | 110.62M | 80.0 sec / 1,000 steps We then perform downstream evaluations on the multilingual [NewsEye](https://zenodo.org/record/4573313#.Ya3oVr-ZNzU) dataset: ![NewsEye hmBERT Evaluation](stats/figures/newseye-hmbert-evaluation.png) # Pretraining ## Multilingual model - hmBERT Base We train a multilingual BERT model using the 32k vocab with the official BERT implementation on a v3-32 TPU using the following parameters: ```bash python3 run_pretraining.py --input_file gs://histolectra/historic-multilingual-tfrecords/*.tfrecord \ --output_dir gs://histolectra/bert-base-historic-multilingual-cased \ --bert_config_file ./config.json \ --max_seq_length=512 \ --max_predictions_per_seq=75 \ --do_train=True \ --train_batch_size=128 \ --num_train_steps=3000000 \ --learning_rate=1e-4 \ --save_checkpoints_steps=100000 \ --keep_checkpoint_max=20 \ --use_tpu=True \ --tpu_name=electra-2 \ --num_tpu_cores=32 ``` The following plot shows the pretraining loss curve: ![Training loss curve](stats/figures/pretraining_loss_historic-multilingual.png) ## Smaller multilingual models We use the same parameters as used for training the base model. ### hmBERT Tiny The following plot shows the pretraining loss curve for the tiny model: ![Training loss curve](stats/figures/pretraining_loss_hmbert-tiny.png) ### hmBERT Mini The following plot shows the pretraining loss curve for the mini model: ![Training loss curve](stats/figures/pretraining_loss_hmbert-mini.png) ### hmBERT Small The following plot shows the pretraining loss curve for the small model: ![Training loss curve](stats/figures/pretraining_loss_hmbert-small.png) ### hmBERT Medium The following plot shows the pretraining loss curve for the medium model: ![Training loss curve](stats/figures/pretraining_loss_hmbert-medium.png) ## English model The English BERT model - with texts from British Library corpus - was trained with the Hugging Face JAX/FLAX implementation for 10 epochs (approx. 1M steps) on a v3-8 TPU, using the following command: ```bash python3 run_mlm_flax.py --model_type bert \ --config_name /mnt/datasets/bert-base-historic-english-cased/ \ --tokenizer_name /mnt/datasets/bert-base-historic-english-cased/ \ --train_file /mnt/datasets/bl-corpus/bl_1800-1900_extracted.txt \ --validation_file /mnt/datasets/bl-corpus/english_validation.txt \ --max_seq_length 512 \ --per_device_train_batch_size 16 \ --learning_rate 1e-4 \ --num_train_epochs 10 \ --preprocessing_num_workers 96 \ --output_dir /mnt/datasets/bert-base-historic-english-cased-512-noadafactor-10e \ --save_steps 2500 \ --eval_steps 2500 \ --warmup_steps 10000 \ --line_by_line \ --pad_to_max_length ``` The following plot shows the pretraining loss curve: ![Training loss curve](stats/figures/pretraining_loss_historic_english.png) ## Finnish model The BERT model - with texts from Finnish part of Europeana - was trained with the Hugging Face JAX/FLAX implementation for 40 epochs (approx. 1M steps) on a v3-8 TPU, using the following command: ```bash python3 run_mlm_flax.py --model_type bert \ --config_name /mnt/datasets/bert-base-finnish-europeana-cased/ \ --tokenizer_name /mnt/datasets/bert-base-finnish-europeana-cased/ \ --train_file /mnt/datasets/hlms/extracted_content_Finnish_0.6.txt \ --validation_file /mnt/datasets/hlms/finnish_validation.txt \ --max_seq_length 512 \ --per_device_train_batch_size 16 \ --learning_rate 1e-4 \ --num_train_epochs 40 \ --preprocessing_num_workers 96 \ --output_dir /mnt/datasets/bert-base-finnish-europeana-cased-512-dupe1-noadafactor-40e \ --save_steps 2500 \ --eval_steps 2500 \ --warmup_steps 10000 \ --line_by_line \ --pad_to_max_length ``` The following plot shows the pretraining loss curve: ![Training loss curve](stats/figures/pretraining_loss_finnish_europeana.png) ## Swedish model The BERT model - with texts from Swedish part of Europeana - was trained with the Hugging Face JAX/FLAX implementation for 40 epochs (approx. 660K steps) on a v3-8 TPU, using the following command: ```bash python3 run_mlm_flax.py --model_type bert \ --config_name /mnt/datasets/bert-base-swedish-europeana-cased/ \ --tokenizer_name /mnt/datasets/bert-base-swedish-europeana-cased/ \ --train_file /mnt/datasets/hlms/extracted_content_Swedish_0.6.txt \ --validation_file /mnt/datasets/hlms/swedish_validation.txt \ --max_seq_length 512 \ --per_device_train_batch_size 16 \ --learning_rate 1e-4 \ --num_train_epochs 40 \ --preprocessing_num_workers 96 \ --output_dir /mnt/datasets/bert-base-swedish-europeana-cased-512-dupe1-noadafactor-40e \ --save_steps 2500 \ --eval_steps 2500 \ --warmup_steps 10000 \ --line_by_line \ --pad_to_max_length ``` The following plot shows the pretraining loss curve: ![Training loss curve](stats/figures/pretraining_loss_swedish_europeana.png) # Acknowledgments Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗
dbmdz/electra-base-turkish-cased-v0-discriminator
6604b2635a2cadb5e83025612d444bd62667855c
2020-04-24T15:57:20.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
dbmdz
null
dbmdz/electra-base-turkish-cased-v0-discriminator
4
null
transformers
18,480
Entry not found
debatelab/cript-large
d4a616673f7d7c3fe9528b3597f64ada9eefb7cf
2021-05-21T15:31:48.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "arxiv:2009.07185", "transformers" ]
text-generation
false
debatelab
null
debatelab/cript-large
4
null
transformers
18,481
--- language: en tags: - gpt2 --- # CRiPT Model Large (Critical Thinking Intermediarily Pretrained Transformer) Large version of the trained model (`SYL01-2020-10-24-72K/gpt2-large-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also: * [blog entry](https://debatelab.github.io/journal/critical-thinking-language-models.html) * [GitHub repo](https://github.com/debatelab/aacorpus) * [paper](https://arxiv.org/pdf/2009.07185)
debatelab/cript
aad306c8713386aeaba3fe2ccb499132fdafd423
2021-05-21T15:40:52.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "arxiv:2009.07185", "transformers" ]
text-generation
false
debatelab
null
debatelab/cript
4
null
transformers
18,482
--- language: en tags: - gpt2 --- # CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer) Small version of the trained model (`SYL01-2020-10-24-72K/gpt2-small-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also: * [blog entry](https://debatelab.github.io/journal/critical-thinking-language-models.html) * [GitHub repo](https://github.com/debatelab/aacorpus) * [paper](https://arxiv.org/pdf/2009.07185)
dee4hf/autonlp-shajBERT-38639804
2787062e6d09b96c51c546d94ffa3e6bcf18eac6
2021-12-04T18:53:26.000Z
[ "pytorch", "albert", "text-classification", "unk", "dataset:dee4hf/autonlp-data-shajBERT", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
dee4hf
null
dee4hf/autonlp-shajBERT-38639804
4
1
transformers
18,483
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - dee4hf/autonlp-data-shajBERT co2_eq_emissions: 11.98841452241473 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 38639804 - CO2 Emissions (in grams): 11.98841452241473 ## Validation Metrics - Loss: 0.421400249004364 - Accuracy: 0.86783988957902 - Macro F1: 0.8669477050676501 - Micro F1: 0.86783988957902 - Weighted F1: 0.86694770506765 - Macro Precision: 0.867606300132228 - Micro Precision: 0.86783988957902 - Weighted Precision: 0.8676063001322278 - Macro Recall: 0.86783988957902 - Micro Recall: 0.86783988957902 - Weighted Recall: 0.86783988957902 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/dee4hf/autonlp-shajBERT-38639804 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("dee4hf/autonlp-shajBERT-38639804", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("dee4hf/autonlp-shajBERT-38639804", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
deepdml/wav2vec2-base-timit-demo-colab
e508b33e9c9be7bf8b7202f4ad8d40dcdbfdd8bd
2022-01-03T15:04:23.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
deepdml
null
deepdml/wav2vec2-base-timit-demo-colab
4
null
transformers
18,484
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4798 - Wer: 0.3474 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5229 | 4.0 | 500 | 1.6557 | 1.0422 | | 0.6618 | 8.0 | 1000 | 0.4420 | 0.4469 | | 0.2211 | 12.0 | 1500 | 0.4705 | 0.4002 | | 0.1281 | 16.0 | 2000 | 0.4347 | 0.3688 | | 0.0868 | 20.0 | 2500 | 0.4653 | 0.3590 | | 0.062 | 24.0 | 3000 | 0.4747 | 0.3519 | | 0.0472 | 28.0 | 3500 | 0.4798 | 0.3474 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.0+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
dehio/german-qg-t5-drink600
aa192bef8376c1d72889bd6789d0e8585fe3a553
2022-01-19T16:38:22.000Z
[ "pytorch", "t5", "text2text-generation", "de", "dataset:deepset/germanquad", "transformers", "question generation", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
dehio
null
dehio/german-qg-t5-drink600
4
null
transformers
18,485
--- license: mit widget: - text: "generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert." language: - de tags: - question generation datasets: - deepset/germanquad model-index: - name: german-qg-t5-drink600 results: [] --- # german-qg-t5-drink600 This model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions. ## Task example #### Input generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert. #### Expected Question Zu welchen Gelegenheiten passt der Monk Sour gut? ## Model description The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600"). We have not yet open sourced the dataset, since we do not own copyright on the source material. ## Training and evaluation data The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg). ## Evaluation It achieves a **BLEU-4 score of 29.80** on the drink600 test set (n=120) and **11.30** on the GermanQUAD test set. Thus, fine-tuning on drink600 did not affect performance on GermanQuAD. In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of **10.76** on the drink600 test set. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
demdecuong/stroke_sup_simcse
28e6817e96f657bda6f8f9e31db2e0d31b9cf55e
2021-06-01T17:17:14.000Z
[ "pytorch", "bert", "feature-extraction", "arxiv:2104.08821", "transformers" ]
feature-extraction
false
demdecuong
null
demdecuong/stroke_sup_simcse
4
null
transformers
18,486
This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821) - Train supervised on 100K triplet samples samples related to stroke domain from : stroke books, quora medical, quora's stroke, quora's general and human annotates. - Positive sentences are generated by paraphrasing and back-translate. - Negative sentences are randomly selected in general domain. ### Extract sentence representation ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_sup_simcse") model = AutoModel.from_pretrained("demdecuong/stroke_sup_simcse") text = "What are disease related to red stroke's causes?" inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs)[1] ``` ### Build up embedding for database ``` database = [ 'What is the daily checklist for stroke returning home', 'What are some tips for stroke adapt new life', 'What should I consider when using nursing-home care' ] embedding = torch.zeros((len(database),768)) for i in range(len(database)): inputs = tokenizer(database[i], return_tensors="pt") outputs = model(**inputs)[1] embedding[i] = outputs print(embedding.shape) ``` ### Result On our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation. - SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain - SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain | Model | Top-1 Accuracy | | ------------- | ------------- | | SimCSE supervised (author) | 75.83 | | SimCSE unsupervised (ours) | 76.66 | | SimCSE supervised + 100k (ours) | 73.33 | | SimCSE supervised + 42k (ours) | 75.83 |
devkushal75/medtextclassifier
09a0a765177974fbf48ab5fe18988595b77d29c4
2021-09-26T10:26:44.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
devkushal75
null
devkushal75/medtextclassifier
4
null
transformers
18,487
Entry not found
devtrent/dummy-model
34342b68fcc7760d09cbd5b98a92d22f6b07e882
2021-07-07T05:58:51.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
devtrent
null
devtrent/dummy-model
4
null
transformers
18,488
# Dummy Model This be a dummmmmy
diegozs97/finetuned-chemprot-seed-0-0k
670789e2309381e3694790e9e7baa8e6262a78fe
2021-12-07T05:07:58.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-0k
4
null
transformers
18,489
Entry not found
diegozs97/finetuned-chemprot-seed-0-100k
de6b8794bf9939a39bed4c51efd3f4f2600f4e52
2021-12-07T05:10:31.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-100k
4
null
transformers
18,490
Entry not found
diegozs97/finetuned-chemprot-seed-0-1800k
b86f0051a28babea2682752f53f631a3d8c4ee68
2021-12-07T05:15:56.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-1800k
4
null
transformers
18,491
Entry not found
diegozs97/finetuned-chemprot-seed-0-200k
60a001f6b0c621b393091cf1c99813a5ba4a4edb
2021-12-07T05:11:33.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-200k
4
null
transformers
18,492
Entry not found
diegozs97/finetuned-chemprot-seed-0-20k
681f726b8de9e91b98c5d103de81c51f0034fcdf
2021-12-07T05:08:43.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-20k
4
null
transformers
18,493
Entry not found
diegozs97/finetuned-chemprot-seed-0-400k
5481235f53e73cb1153621a785902e4ddd0a4ceb
2021-12-07T05:12:18.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-400k
4
null
transformers
18,494
Entry not found
diegozs97/finetuned-chemprot-seed-0-60k
846859dfc21ce2dd0631ebc704843aab21756ea0
2021-12-07T05:09:46.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-60k
4
null
transformers
18,495
Entry not found
diegozs97/finetuned-chemprot-seed-0-700k
6fdbd39de344d14e27c4330053cda334a4309b47
2021-12-07T05:13:23.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-0-700k
4
null
transformers
18,496
Entry not found
diegozs97/finetuned-chemprot-seed-1-1500k
edf0c62140b32de1cde47d96c6009df846b8689a
2021-12-07T05:24:52.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-1-1500k
4
null
transformers
18,497
Entry not found
diegozs97/finetuned-chemprot-seed-1-1800k
d77f00ee12b3087acd2a60d636ac8c5232fcb767
2021-12-07T05:25:56.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-1-1800k
4
null
transformers
18,498
Entry not found
diegozs97/finetuned-chemprot-seed-1-200k
ddd61a1812234b610a7fc89bef81f63996988bd5
2021-12-07T05:21:18.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-chemprot-seed-1-200k
4
null
transformers
18,499
Entry not found